Value Of Side Lobe Level Research Articles

Effective Minimization of Side Lobe Level of Sparse Thinned Planar Array Antenna in Multiple Planes with Constraints

ABSTRACT In this paper, the authors propose to design thinned sparse planar array antenna in multiple planes using meta-heuristic optimization algorithm such as Teaching Learning-Based Optimization (TLBO) algorithm. The objective is to achieve the minimum value of side lobe level (SLL) in the radiation pattern in two vertical planes as well as in all vertical planes. The optimal positions of the isotropic antennas in the array are obtained having multiple optimization constraints such as total number of antennas in the array, aperture area and minimum inter-element spacing. The simulation results demonstrate that the proposed method is found to be relatively simpler but promising compared to the results reported using other competitive methods.

NULL STEERING BEAMFORMER USING HYBRID ALGORITHM BASED ON HONEY BEES MATING OPTIMISATION AND TABU SEARCH IN ADAPTIVE ANTENNA ARRAY

In this article, a new hybrid algorithm based on Honey Bees Mating Optimization (HBMO) combined with the Tabu Search (TS) for null steering beamformer in adaptive antenna array is presented. The proposed method HBMO/TS is applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. Moreover, the proposed algorithm is tested and compared with two other well-known approaches that are the Least Mean Squares (LMS) and Genetic Algorithm (GA). The abovementioned methods have been performed considering uniform linear antenna array and achieved by controlling only the phase of each array element. Results obtained prove the efiectiveness of our proposed approach HBMO/TS.

ADAPTIVE BEAMFORMING WITH LOW SIDE LOBE LEVEL USING NEURAL NETWORKS TRAINED BY MUTATED BOOLEAN PSO

A new adaptive beamforming technique based on neural networks (NNs) is proposed. The NN training is accomplished by applying a novel optimization method called Mutated Boolean PSO (MBPSO). In the beginning of the procedure, the MBPSO is repeatedly applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. The estimated weights are used to train e-ciently a NN. Finally, the NN is applied to a new set of random cases and the extracted radiation patterns are compared to respective patterns extracted by the MBPSO and a well-known robust adaptive beamforming technique called Minimum Variance Distortionless Response (MVDR). The aforementioned comparison has been performed considering uniform linear antenna arrays receiving several interference signals and a desired one in the presence of additive Gaussian noise. The comparative results show the advantages of the proposed technique.

COMPARATIVE STUDY OF NEURAL NETWORK TRAINING APPLIED TO ADAPTIVE BEAMFORMING OF ANTENNA ARRAYS

This paper presents a comparative study of neural network (NN) training. The trained NNs are used as adaptive beamformers of antenna arrays. The training is performed either by a recently developed method called Mutated Boolean PSO (MBPSO) or by a well known beamforming method called Minimum Variance Distortionless Response (MVDR). The training procedure starts by applying the MBPSO and the MVDR to a set of random cases where a linear antenna array receives a signal of interest (SOI) and several interference signals at random directions of arrival (DOA) difierent from each other in the presence of additive Gaussian noise. For each case, the MBPSO and the MVDR are independently applied to estimate respective excitation weights that make the array steer the main lobe towards the DOA of the SOI and form nulls towards the DOA of the interference signals. The lowest possible value of side lobe level (SLL) is additionally required. The weights extracted by the MBPSO and the weights extracted by the MVDR are used to train respectively two difierent NNs. Then, the two trained NNs are independently applied to a new set of cases, where random DOA are chosen for the SOI and the interference signals. Finally, the radiation patterns extracted by the two NNs are compared to each other regarding the steering ability of the main lobe and the nulls as well as the side lobe level. The comparison exhibits the superiority of the NN trained by the MBPSO.

DESIGN OF FULLY DIGITAL CONTROLLED RECONFIGURABLE DUAL-BEAM CONCENTRIC RING ARRAY ANTENNA USING GRAVITATIONAL SEARCH ALGORITHM

In this paper, we propose an optimization method based on Gravitational Search Algorithm (GSA) for design of reconflgurable dual-beam concentric ring array of isotropic elements with phase only control of flve-bit digital phase shifters. The problem is to flnd a common radial amplitude distribution using four-bit digital attenuator that will generate three difierent types of broadsided beam pair in vertical plane: a pencil/pencil beam pair, a pencil/∞at-top beam pair and a ∞at-top/∞at-top beam pair (sector pattern) with desired value of side lobe level, flrst null beam width and ripple. The two patterns difier only in radial discrete phase distribution while sharing a common discrete radial amplitude distribution. The optimum sets of four- bit discrete radial amplitude distribution generated by four-bit digital attenuators and flve-bit discrete radial phase distribution generated by flve-bit digital phase shifters for obtaining dual radiation patterns are computed by Gravitational Search Algorithm.

Improving the radiation characteristics of a base station antenna array using a particle swarm optimizer

AbstractA particle swarm optimization based technique is applied on linear antenna arrays used by broadcasting base stations. Both the geometry and the excitation of the antenna array are optimized by a suitable algorithm under the constraints of the maximum possible gain at the desired direction and the desired value of side lobe level. The matching condition of the elements of the antenna array is also required by the algorithm. The technique has been applied to antenna arrays composed of collinear wire dipoles and seems to be very promising for improving radiation patterns of base station antenna arrays in many practical applications. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 1690–1698, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22505